[Dy2stat] Support lambda and enhance transformation of IfExpr (#24530)

* fix bug with `if Tensor` in is_control_flow test=develop

* remove continue test=develop

* Support lambda and add unittest test=develop

python/paddle/fluid/dygraph/dygraph_to_static/ifelse_transformer.py

@@ -101,8 +101,18 @@ class IfElseTransformer(gast.NodeTransformer):
         self.generic_visit(node)
         if need_transform:
             pred_node, new_assign_nodes = if_condition_visitor.transform()
+
+            if len(new_assign_nodes) > 0:
+                pred_node = merge_multi_assign_nodes(new_assign_nodes)
+
             new_node = create_cond_node(None, pred_node, node.body, node.orelse,
                                         True)
+            # Note: A blank line will be added separately if transform gast.Expr
+            # into source code. Using gast.Expr.value instead to avoid syntax error
+            # in python.
+            if isinstance(new_node, gast.Expr):
+                new_node = new_node.value
+
             return new_node
         else:
             return node
@@ -145,6 +155,59 @@ class IfElseTransformer(gast.NodeTransformer):
         return self.new_func_nodes
 
 
+def merge_multi_assign_nodes(assign_nodes):
+    """
+     Merges multiple separate assign statements into a single node.
+    """
+    if not isinstance(assign_nodes, (list, tuple)):
+        assign_nodes = [assign_nodes]
+
+    return MergeAssignTransformer().transform(assign_nodes)
+
+
+class MergeAssignTransformer(gast.NodeTransformer):
+    """
+    Merges multiple separate assign statements into a single node.
+    Because it cannot be determined the insertion location of new nodes for `IfExpr`,
+    so replaces original node with merges conditional node.
+
+    Note: This is a very low level api and only used for IfExpr transformation
+          in control flow.
+
+    For example:
+        IfExpr:
+            y = x+1 if mean or x > 0 else x-1
+
+        assign nodes:
+            bool_tensor_1 = fluid.layers.cast(x=mean, dtype='bool')
+            logic_or_0 = fluid.layers.logical_or(x=bool_tensor_1, y=x > 0)
+
+        merged node:
+            fluid.layers.logical_or(x=fluid.layers.cast(x=mean, dtype='bool'), y=x > 0)
+    """
+
+    def __init__(self):
+        self._name_to_nodes_value = {}
+
+    def transform(self, nodes):
+        value = None
+        for node in nodes:
+            assert isinstance(node, gast.Assign)
+            # Note: targets of created assign node in control flow `if`
+            # only contains one element.
+            assert isinstance(node.targets[0], gast.Name)
+            target_name = node.targets[0].id
+            value = self.visit(node.value)
+            self._name_to_nodes_value[target_name] = value
+
+        return value
+
+    def visit_Name(self, node):
+        if node.id in self._name_to_nodes_value:
+            node = self._name_to_nodes_value[node.id]
+        return node
+
+
 class NodeTestTransformer(gast.NodeTransformer):
     def __init__(self,
                  ast_node,

python/paddle/fluid/tests/unittests/dygraph_to_static/test_lambda.py

@@ -0,0 +1,102 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import numpy as np
+import unittest
+import paddle.fluid as fluid
+
+from paddle.fluid.dygraph import declarative
+
+
+def call_lambda_as_func(x):
+    x = fluid.dygraph.to_variable(x)
+
+    add_func = lambda x, y: x + y
+    mean_func = lambda x: fluid.layers.mean(x)
+
+    y = add_func(x, 1)
+    y = add_func(y, add_func(y, -1))
+    out = mean_func(y)
+
+    return out
+
+
+def call_lambda_directly(x):
+    x = fluid.dygraph.to_variable(x)
+
+    y = (lambda x, y: x + y)(x, x)
+    out = (lambda x: fluid.layers.mean(x))(y)
+
+    return out
+
+
+def call_lambda_in_func(x):
+    x = fluid.dygraph.to_variable(x)
+
+    add_func = lambda x: x + 1
+
+    y = fluid.layers.mean((lambda x: fluid.layers.relu(x))(x))
+    out = add_func(y) if y > 1 and y < 2 else (lambda x: x**2)(y)
+
+    return out
+
+
+def call_lambda_with_ifExpr(x):
+    x = fluid.dygraph.to_variable(x)
+
+    add_func = lambda x: x + 1
+
+    y = fluid.layers.mean(x)
+    out = add_func(y) if y or y < 2 else (lambda x: x**2)(y)
+
+    return out
+
+
+class TestLambda(unittest.TestCase):
+    def setUp(self):
+        self.x = np.random.random([10, 16]).astype('float32')
+        self.x = np.array([1, 3]).astype('float32')
+        self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        self.init_func()
+
+    def init_func(self):
+        self.dyfuncs = [
+            call_lambda_as_func, call_lambda_directly, call_lambda_in_func,
+            call_lambda_with_ifExpr
+        ]
+
+    def run_static(self, func):
+        return self.run_dygraph(func, to_static=True)
+
+    def run_dygraph(self, func, to_static=False):
+
+        with fluid.dygraph.guard(self.place):
+            x_v = fluid.dygraph.to_variable(self.x)
+            if to_static:
+                ret = declarative(func)(x_v)
+            else:
+                ret = func(x_v)
+            return ret.numpy()
+
+    def test_ast_to_func(self):
+        for func in self.dyfuncs:
+            self.assertTrue((self.run_dygraph(func) == self.run_static(func)
+                             ).all())
+
+
+if __name__ == '__main__':
+    unittest.main()